Automated assay for identification of individual cells during kinetic assays

ABSTRACT

The present invention provides methods and software for tracking individual cells during a kinetic cell screening assay.

CROSS REFERENCE

[0001] This application claims priority to U.S. Provisional Applicationfor patent serial No. 60/258,147 filed Dec. 22, 2000.

FIELD OF THE INVENTION

[0002] The application relates to kinetic cell-based screening.

BACKGROUND OF THE INVENTION

[0003] Kinetic assays are performed by making measurements at a seriesof points in time to measure the change of a sample. The measurements atany one time point might also be used for a non-kinetic assay, herecalled a fixed endpoint assay. Fixed endpoint assays are sufficient forsamples that exhibit little or no change over the duration of the assay.If the sample changes over time, kinetic measurements are required tomeasure those changes. Mathematical descriptions of the trends invarious cell parameters over time represent kinetic features that aredistinct from the measurements calculated in fixed endpoint assays.

[0004] Kinetic assays are performed on the same sample over time and aredistinct from common experiments that provide an approximation ofkinetic features with fixed endpoint assays on different portions of asample. For example, if the sample is a population of cells comprising anumber of similar individual cells, changes in the population over timecan be measured by assaying portions of the sample with a series offixed endpoint assays. This approach is commonly used in biochemical orimmunohistochemical assays when samples are killed (i.e., fixed) ordestroyed during the assay. A series of fixed endpoint assays makesmeasurements on individual cells, but the particular individuals withineach population are different at each fixed endpoint assay and cannot berelated to each other on the cell level. A series of fixed endpointassays provides useful kinetic information only when the populationaverage measurements are assumed to be related from portion to portionof the sample and the individual cells in the population are assumed tobe equivalent.

[0005] The fixed endpoint approach is insufficient if the cells in thesample are not equivalent or if the changes must be related over time ona cell-by-cell basis. Measurements of physiologically relevant cells areheterogeneous, reflecting the normal variability of cell behavior in anintact animal. The heterogeneity often includes important information onthe physiology of cells in the living state, and biologically relevantmeasurements must include, not exclude the variability of the sample.Living cells that change independently of each other must be measured atmultiple times and the measurements correlated over time on acell-by-cell basis.

[0006] A true kinetic assay addresses problems by providing measurementson single cells correlated through time. Generally, cells are identifiedby position and by other characteristics to provide continuity of celllevel biological measurement at each time. A typical problem to beovercome is positional uncertainty of cells due to movement of cells orthe measuring instrument. The ability to identify cells over time allowsthe user to measure and account for sample variability, andsubpopulation behavior. The whole population response of a sample isoften due to the activity of just a subpopulation of cells. Accuratekinetic measurement of subpopulations provides higher contentinformation about physiological, or pharmacological response of abiological sample. Cell-based kinetic measurements also allow multiplemeasurements of the same sample (multiparametric assays) to becorrelated on the cell level, connecting measurements of differentcellular functions and mechanisms, and thus providing a bettermechanistic understanding of cells and drugs that affect them.

[0007] Therefore, methods for tracking individual cells during a kineticcell screening assay are needed in the art.

SUMMARY OF THE INVENTION

[0008] The present invention provides methods and software for trackingindividual cells during a kinetic cell screening assay, comprising:

[0009] a) providing cells that possess at least a first luminescentlylabeled reporter molecule that reports on a cell structure;

[0010] b) obtaining a structure image from luminescent signals from theat least first luminescently labeled reporter molecule in the cells in afield of view;

[0011] c) creating a structure mask for individual cells in the field ofview;

[0012] d) defining a reference point of each structure mask;

[0013] e) assigning an cell identification to each reference point inthe field of view;

[0014] f) repeating steps (b) through (e) at a second time point;

[0015] g) correlating cell identification between the first time pointand the second time point by calculating a distance between referencepoints in the field of view at the first time point and reference pointsin the field of view at the second time point; and

[0016] h) defining a cell identification match by identifying referencepoints in the field of view at the first time point and reference pointsin the field of view at the second time point that are closest together.

[0017] In a preferred embodiment, steps (f)-(h) are repeated a desirednumber of time points, wherein determining the distance betweenreference points is done by determining a distance between referencepoints in successive time points, and wherein defining the closest cellidentification match is done by defining the closest cell identificationmatch in successive time points.

[0018] In another preferred embodiments include assigning a qualityscore to the cell identification match based on a distance determinedfor a second closest cell identification match, wherein a cellidentification match is rejected if the quality score is below auser-defined threshold for a quality score.

[0019] A further preferred embodiment comprises comparing other featuresof the individual cells between successive time points in order tofacilitate cell identification.

BRIEF DESCRIPTION OF THE FIGURES

[0020]FIG. 1 is a flow chart showing one embodiment of the method fortracking individual cells during a kinetic cell screening assay.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0021] In kinetic assays, cells may move around, enter or leave thefield, grow, shrink, or divide; also, separate cells may move into orout of contact with each other. In determining features for individualcells over time, it is preferable to optimize correct identification ofindividual cells from timepoint to timepoint. Thus, after collecting thedata for a current timepoint, a second cell identification is reconciledagainst a cell identification obtained from the first timepoint in thekinetic scan for the well. This will ensure that the kinetic data isassociated with the correct cell throughout all timepoints of thekinetic scan. After obtaining the cell, field, well, and plate leveldata for the current timepoint, the kinetic data is integrated with anyprevious kinetic data to form the kinetic features for individual cells,from which field-based, well-based, and/or plate-based kinetic featurespertaining to any desired cell screening assay can be derived.

[0022] Methods for reconciling cell identification across different timepoints help insure that any given cell has the same identification fromimage to image in the image series.

[0023] The present invention provides methods and software for trackingindividual cells during a kinetic cell screening assay, comprising:

[0024] a) providing cells that possess at least a first luminescentlylabeled reporter molecule that reports on a cell structure;

[0025] b) obtaining a structure image from luminescent signals from theat least first luminescently labeled reporter molecule in the cells in afield of view;

[0026] c) creating a structure mask for individual cells in the field ofview;

[0027] d) defining a reference point of each structure mask;

[0028] e) assigning an cell identification to each reference point inthe field of view;

[0029] f) repeating steps (b) through (e) at a second time point;

[0030] g) correlating cell identification between the first time pointand the second time point by calculating a distance between referencepoints in the field of view at the first time point and reference pointsin the field of view at the second time point; and

[0031] h) defining a cell identification match by identifying referencepoints in the field of view at the first time point and reference pointsin the field of view at the second time point that are closest together.

[0032] As used herein, the term “image” means a digital representationof the optically detectable signals from the at least first opticallydetectable reporter molecule, and does not require a specificarrangement or display of the digital representation. Images are parcelsof information derived from the sample that are organized in variousways for the convenience of the observer. In preferred embodiments, wellknown formats for such “images” are employed, including but not limitedto DIB, TIFF, BMP, picture element (pixel) maps, three-dimensionalvolume arrays, two dimensional surface or cross section arrays, or onedimensional line scan images, oscilloscope time traces, orthogonalarrays of integers, pixel intensity numbers, hexagonal grids ofintegers, floating point pixels, and planar, chunky or Bayer patternarrays of multispectral pixel arrays. In a most preferred embodiment,picture element (pixel) map images are used, such as those produced byoptical cameras where spatial location in one plane (X, Y) within thesample is represented by spatial location within the map (x, y) andluminescent sample intensity (I) is represented by the signal amplitudeor value (i) at each pixel.

[0033] The Field Of View (FOV) is the area that is imaged. It isequivalent to the image size. The dimension of the FOV can either beexpressed in microns at the scale of the sample area, or in pixels ofthe image size. The cell sample area is generally much larger than theFOV, such as for a medium or high resolution image of a 96, or 384 wellplate.

[0034] As used herein an “optically detectable reporter molecule” is areporter molecule that can emit, reflect, or absorb light, and includes,but is not limited to, fluorescent, luminescent, and chemiluminescentreporter molecules. In a preferred embodiment, a fluorescent reportermolecule is used.

[0035] The cell structure reported on by the optically detectablereporter molecule can be any detectable cell structure, includingnuclei, intracellular organelles, cytosol markers, and plasma membranemarkers. In the simplest case, the cell structure is present as a singleentity in the cell, such as the nucleus.

[0036] As used herein, the reporter molecule “reports on” the cellstructure by processes including, but not limited to, binding to thecell structure, either directly or indirectly, and by being incorporatedinto or contained within the cell structure.

[0037] As used herein, the “reference point” is a single point definedrelative to the cell structure, including but not limited to a center ofthe cell structure, a center of mass of the cell structure, a centroid(defined as a geometric center) of the cell structure, or by drawing abounding box around the cell structure, wherein the point can bedefined, for example, as the intersection of any two diagonals withinthe bounding box. In a preferred embodiment, a centroid of the cellstructure is used. Images are acquired of the at least first opticallydetectable reporter molecule, and the images can optionally bepreprocessed (shade corrected and smoothed). The images are thenthresholded (preferably using an automatic thresholding procedure),producing a structure mask. In a further preferred embodiment, the cellstructure is a nucleus, wherein the structure image is a nuclear image,and wherein the structure mask is a nuclear mask. As used herein, theterm “mask” means a processed version of the cell structure image tofill holes. Creation of a mask preferably comprises thresholding theimage to select relevant image components with values (position,intensity) above background outside of the structures of interest.

[0038] As used herein, the following terms are defined as below:

[0039] A cell that is entirely within the FOV is termed an “FOV cell”.These are the cells that can be analyzed.

[0040] A cell that is entirely outside the FOV is a “Non-FOV cell”.These cells are not analyzed.

[0041] A Boundary Cell is defined as a cell touching the FOV boundary.Most feature measurements of these cells would be incomplete orinaccurate, and thus Boundary Cells are preferably discounted. However,a Boundary Cell can be considered an intermediate state that can betracked if desired.

[0042] A Departure is defined as a cell leaving, in its entirety, theFOV from any direction. The cell needs to be completely outside the FOVto be called a Departure.

[0043] Cells in motion may arrive and depart from the FOV at any time.An Arrival is defined as a cell entering, in its entirety, the FOV fromany direction. The cell needs to be completely inside the FOV to becalled an Arrival, because until then, it would be an incompleteboundary cell that is generally not analyzed.

[0044] Arrivals and Departures add to the complexity of tracking becausethey require a more complex administration of which cells existthroughout the extent of the entire movie. If all cells exist at alltime points, this administration would be a simple array that can beestablished from analysis of the first time point. If cells are notpresent at certain time points, it requires an analysis of the fullimage series to build up this inventory and more elaborate datamanagement.

[0045] A Create event is defined as a cell appearing “out of the blue”anywhere in the FOV but not on the edge. For example, a cell may nothave had enough labeling intensity to be detected at first, but duringthe course of the image series it responded and became visible. If acell appears on the edge, it would be an arrival. A Destroy event isdefined as a cell disappearing from the FOV but not moving out as adeparture. For example, a cell may die and somehow lose its labelingmarker).

[0046] There are three general embodiments of the methods and softwarefor tracking individual cells during a kinetic cell screening assay ofthe invention. Each method provides alternatives to the basic method,each with added sophistication for rejecting fewer cells and providingincreased robustness.

[0047] 1. Simple Proximity Method: In one embodiment, determining a cellidentification match comprises identifying reference points in the fieldof view at a first time point and reference points in the field of viewat a second time point that are closest together, and assigning theappropriate cell identification to the cell at the later time point inthe image series. This should be successful if most or all of the cellsare slow moving (considering the frame rate). Any other data needed todo the comparison at subsequent time points in the image series isstored, including but not limited to the reference points of all thecells from the immediately preceding time point, and the cell ID's whichwere assigned to each of those cells (ID Mapping Table). In a preferredembodiment, the cell identification match is rejected if it falls belowa user-defined threshold for a cell identification match. For example,the user can determine a maximum reasonable distance that cells can movebetween time point (i.e. a maximum rate of motion), or thresholding canbe used to select relevant image components with values (position,intensity) above background outside of the structures of interest.

[0048] The successive sets of reference points is preferably matched upas follows. For each cell in the current set, its distance to eachreference point in the immediately preceding timepoint is determined.The two closest preceding cells are determined. The closest previouscell is assigned to the current cell, and a quality score (between 0 and100) is assigned to the match, which increases as the relative distanceof the second best match increases. In a preferred embodiment, thequality score is calculated according to the formula:

Quality=100*(SecondBestDistance−BestDistance)/SecondBestDistance

[0049] This is preferably used when the distances that the cells movedare small enough so that there is not confusion as to which cell movedwhere. A quality of match is computed to estimate this. The quality ofmatch is 100% if there is no confusion, and 0% if there is an equalchance that the cell could have been a neighboring cell. In a furtherpreferred embodiment, a cell identification match is rejected if thequality score is below a user-defined threshold for a quality score. Thethreshold can be defined in various ways, such as those described above,or, given a specific experimental situation for the cells, the user canpredict the likelihood of cells being created or destroyed and theacceptable quality score of matching can be set accordingly. Forexample, the threshold for an acceptable quality of match score can beset be lower if the user is not expecting Create and Destroy events.Cells/artifacts are removed from analysis if they do not map uniquely toa cell ID.

[0050] 2. Total Distance Minimization: If the Simple Proximity Methodresults in ambiguous matches (e.g. low quality scores due to two cellsequidistant), a global matching may be performed as well. Thus, in afurther embodiment, the method further comprises determining a total sumof all distances or distances squared for all possible cellidentification matches in successive time points, wherein a smallesttotal sum of all distances or distances squared is defined as a closestset of cell identification matches.

[0051] A matrix of distances between each current and each previous cellis computed. Every possible permutation of the cells, [there are N!permutations]) is scored by summing the distances (or the squares ofdistances) for all its pairs, with the lowest total being the best cellidentification match. In a further preferred embodiment, the amount ofcomputation can be reduced by pre-pairing (using the Simple ProximityMethod) any matches with quality scores over a preset threshold, andthen excluding the cells in those pre-pairings from the global matchingprocess. This last adjustment will work very well if the cells varywidely in movement rate. Alternatively, the method can reduce the amountof computation by excluding those cells in pre-pairing that fall below auser define threshold from the global matching process.

[0052] In a further preferred embodiment, the methods further compriseassigning a quality score to the cell identification match based on asum of distances or distances squared determined for a second closestcell identification match, and wherein a cell identification match isrejected if the quality score is below a user-defined threshold for aquality score.

[0053] 3. Total Distance & Feature Matching Minimization: In a furtherpreferred embodiment, defining the cell identification match furthercomprises comparing other characteristic features of the individualcells between successive time points in order to identify cells, andcomparing the measurements for any proposed match. Applied to individualcells, this is a way of efficiently resolving individual ambiguities. Aspart of a more elaborate method (e.g.: as a follow up to the TotalDistance Minimization Method), the feature sets constitute a matrix ofbetter data, which is compared with the vectors for the previoustimepoint, and minimizing the weighted sum of differences (ordifferences squared) as the measure of matching. The matrix would now bebetter called a confusion matrix, where each position is a compoundednumber containing the distance+any other cell feature matching values.

[0054] Quality of Cell Match

[0055] In contrast to the measurement of a quality score based on thesimple proximity method, a quality score for the Total Distance &Feature Matching Minimization method is further based on quality of thematch based on one or more of any number of cell features, including butnot limited to a) actual available features of the cell or subcellularstructures, such as fluorescent intensity, cell area, cell shape, etc.;and/or b) additionally created features of the cell such as exogenoustags (i.e.: tags associated with the cells, solely for the purpose ofcell tracking), such as “bar-coding tags” (discussed below). Thealgorithm is designed to work with any set and any number of features,which may change for different assays, cell types, etc.

[0056] While the analysis of cell features in determining a qualityscore can be incorporated into the simple proximity method (e.g.:carried out for possible cell identification matches being analyzed), itis preferred to “pre-pair” cell identification matches via the simpleproximity method, and carry out feature analysis only when necessary onthose cell identification matches that are ambiguous using the simpleproximity method.

[0057] Since the cell can change shape and other cell features overtime, the quality score is never absolutely perfect. Conversely,different cells may possess similar cell features, and thus can yield arelatively high score for the quality score. Each cell feature may havea different value of contribution to the matching problem. Cell featuresthat have more variation between cells, such as a unique identifier(nuclear texture, intensity, or position with respect to other cellstructures such as the perinuclear Golgi apparatus are preferablyaccorded more weight than those that show less variation between cellssuch as the position of the whole cell reference point with respect to anuclear reference point. This preferred embodiment comprises according aweight factor for each cell feature for calculation of the qualityscore. In one embodiment, a user provides those weight factors. Inanother embodiment, the weight factor is computed from learning sets andapplying a Bayes classifier or other technique.

[0058] In a preferred embodiment, the quality score is determined byfirst calculating its reciprocal, i.e. the difference between cells.This “Mismatch” (preferably weighted) is the sum of the differencesbetween cell features. In a preferred embodiment, the MisMatch betweenan cell 1 and cell 2 is expressed as follows: Where: “a” is each cellfeature being used W_(a) is the weight factor for feature a F_(a1) isthe feature a computed for cell 1 DIFF(F_(a1), F_(a2)) is the differencefunction between cell feature a computed for cell 1 and cell feature acomputed for cell 2.

[0059] The DIFFO function can be defined, for example, as:

DIFF(x,y)=ABS(x−y);

[0060] (wherein “ABS” means the “absolute number”) or

DIFF(x,y)=(x−y)·(x−y)

[0061] The square of the difference helps in making the function“steeper”.

[0062] For example, one or more of the following cell features can beassessed:

[0063] a) Cell size

[0064] b) Average cell fluorescent intensity

[0065] c) Cell P2A or shape factor

[0066] For these features, the weight factor (Wa, Wb and Wc,respectively) are preferably set to 1.0. For example, the weight of eachcell feature can be reduced by using a weighting factor that is afraction between 0 and 1 while the weight of each cell feature can beincreased by using a weighting factor greater than 1. The array ofweight factors is given as an input to the algorithm, so it can beeasily adapted as needed.

[0067] The quality score is simply the reciprocal of the MisMatch:

quality score−1/MisMatch

[0068] For a “perfect match” the MisMatch is zero, and hence the qualityscore is infinitely good.

[0069] Possible Limitations on the Quality Score

[0070] In some instances, the cells may be too “plain” to extractdistinctive cell features from them. For example, they may all look likespheres without texture. One way to alleviate this problem is to examineas many unique cell features as possible. For example, multiplefluorescence channels can be analyzed to generate more cell features,for example by labeling multiple structures such as nuclei and Golgiapparatus. Generally, the desirable characteristics of cell features foridentifying cells include distinction from neighboring cells andconstancy over time.

[0071] In a preferred embodiment, a “Bar Coding” scheme is implementedto get even more distinct features added to the cells. Generally, thedesirable characteristics of barcoding particles for identifying cellsinclude distinction from each other, from cell-to-cell, and constancyover time. Particles for “bar-coding” cells are available in mixtures ofvarying intensity, color and size (fluorescent beads of different sizeand intensity from Bangs Labs, or sets of multispectral Quantum Dotscontained within beads, for example), so that most cells can beassociated with a particle or set of particles possessing uniquefeatures which can thus be uniquely accounted as unique cell features.Bar code particles can be contained within cells by random distributionto cells and natural phagocytosis of the particles. Alternate methodscan be employed to increase the yield of labeled particles and theuniformity of labeling, including physical projection or injection ofparticles, or by depositing cells onto ordered arrays of barcodeparticles deposited on substrates to control the number and distributionof particles delivered to cells. Barcode particles need not beassociated on a perfect one-to-one basis with cells to provide value forcell identification. The methods described here are fault tolerant andimperfect bar coding contributes to cell identification even if barcodesare not contained within every cell or if barcodes are repeatedoccasionally within the image. Barcode particles can be observationallyassociated with cells by, for example, their proximity to a labelednuclei or other cell structure or by being contained within the cellperiphery. In these instances, the “bar code” features are treated justlike any other cell feature in the quality score equation above. Infavorable instances, distribution, uniqueness and universality of thebar coding particles is sufficient and no supportive biologicalstructures are required to associate particles with unique cells. If thebar coding technique is very high quality with a majority of cellcontaining a unique barcode, the weight factor for the bar code cellfeature can be very high, completely supplanting the need to labelendogenous cell structures. A less stringent bar coding scheme is givena lower weight factor and simply contributes a part in the process ofmatching.

[0072] In other instances, the cells may change their shape and cellfeatures so much when imaged from timepoint to timepoint that theiridentification at different time points as the same cell is difficult.This problem is alleviated by sampling often enough in time (increasedSampling Frequency), to ensure the variability over time is less thanthe variability between cells.

[0073] The Sampling Frequency means the number of image acquisitions perminute. An insufficient sampling frequency reduces the ability toeffectively track cells, or measure fast cellular events. An excessivesampling frequency may damage the cells due to phototoxicity. An optimalsampling frequency will thus vary depending on various factors,including cell motion, cell density, the cellular event being analyzed,and the probabilities of arrivals (move into FOV), departures (leaveFOV), and collisions with other cells. For example high speed calciumchanges may require a faster sampling frequency to satisfy the trackingneeds than most assays. In general, an optimal sampling frequency is theminimum frequency needed to be able to reconstruct a signal witharbitrary precision (ie: the Nyquist sampling frequency). One way tofind this frequency is to look at the Fourier spectrum of the originalsignal and find the highest frequency component. The Nyquist samplingfrequency is twice that frequency. Sampling below the Nyquist frequencymay not allow reconstruction of the higher frequency components of thesignal and, may produce aliasing artifacts.

[0074] It is also desirable to optimize the “Yield” of the kineticassay. The yield can be expressed as an absolute number of cells thatmaintain a Free Path (i.e.: no collisions), or as the percentage ofthose cells compared to the total cells. The probability of a Free Pathis the likelihood of a cell not being involved in any collisions, andnot leaving or entering the FOV during the entire image acquisition.This probability will go down the longer kinetic data is acquired, sincesufficient cell motion can eventually cause all cells to collide or movefrom the FOV, and is dependent on the cell motion and the cell density.

[0075] Given a particular cell density (e.g.: number of cells per squarearea), a user can compute average distance between cells. If a FOV hasan extremely high cell density, it will have the potential for a highyield, but probably all cells will be colliding within the first fewimage acquisitions, reducing the yield to zero. Hence, it is useful todetermine an optimal cell density to produce an optimal yield, for agiven set of cell behavior parameters. The optimal cell density willvary based on all of the various factors discussed herein, and thus ispreferably determined experimentally or by computer simulation. Forexample, the optimal experimental cell density will depend on thebiological function of cells to be measured and on the statistical errordesired for measurement of the sample.

[0076] An optimal cell density, accounting for biological variables, isbetween 10 and 50% of confluency.

[0077] The average distance between cells may need to be corrected forcell confluency (e.g.: percentage of cells that are touching othercells) or cell clustering. Given an average cell motion speed estimate,we can set the maximum sampling frequency allowed to satisfy the“Nyquist” criterion. The cell motion speed is preferably expressed as anaverage distance traveled per time point of the image acquisition. Cellmotion can also be described by defining its speed and persistence in adirection (Directed motion), by a diffusion coefficient (Brownianmotion), and/or by defining an affinity factor, which reflects theeffect of nearby cells on the motion of a cell.

[0078] Rolling Average of the Quality Score

[0079] The quality score can be averaged over multiple time points andapplied to a later time point as an average quality score. This “rollingaverage” will become part of the feature vector computed for each cellat each timepoint. This way, it is carried forward during the analysisof each image acquisition, without the need to access the entire imageacquisition series.

[0080] In a preferred embodiment, at time point t, this is defined as:

Average quality score_(t)=(1−k)·Average quality score_((t−1)) +k·qualityscore_(t)

[0081] where k is constant to define the weight factor of this geometricaverage. The value of k can be determined experimentally by providingthe best fit with sample truth data where cell identification ispre-determined. The choice of k depends on the sampling frequencyrelative to the amount of change in the cells, and on the desired amountof smoothing of the feature over time. A value of k close to 1 will dolittle or no smoothing, while a value close to zero will do a lot ofsmoothing. The average quality score value is set it with a value thatreflects expectation at the beginning of the image acquisition series.The method does not require control or truth data but the parametersused to calibrate the method for a specific biological sample arepreferably derived experimentally from control data that matches theexperimental sample in the measures used for cell identification. Forexample, control experiments can be run, or a reasonable expectation forthat value can be provided.

[0082] 4. Reduction of the Confusion Matrix: The computational cost ofthe Total Distance & Feature Matching Minimization method and a completeconfusion matrix can be quite high, and grows rapidly with the number ofcells. Therefore, in a preferred embodiment, the computationally lessintensive Simple Proximity method is used first, and only those cellidentification matches that are ambiguous are subjected to confusionmatrix analysis, as necessary.

[0083] The strategy looks as follows:

[0084] a) Try to match cells based on Simple Proximity

[0085] b) Identify problem areas where Simple Proximity may not work

[0086] c) Compute confusion matrices for those areas—on a limited set ofcells

[0087] d) Solve confusion matrices for the problem areas

EXAMPLES

[0088] Determining When the Simple Proximity Method is Insufficient

[0089] As described in the previous version, we can consider twoexamples. Then we proceed with the strategy to assign the right methodto the job.

[0090] 1. Very Simple Case

[0091] Three cells (a, b and c), each one moves a bit to the right andthey will become A,B and C in the next time point: a A b B c C //Program output . . . Test 1 . . . 3 cells move to the right PreviousImage: Label Xcm Ycm CellID 0 100 100 1 1 200 200 2 2 300 300 3 NewImage: Label Xcm Ycm CellID Quality New? dY dX Distance 0 120 100 1 100%Old 20 0 20.00 1 225 200 2 100% Old 25 0 25.00 2 330 300 3 100% Old 30 030.00 Distance matrix: 0 1 2 0 20.00 128.06 269.07 1 160.08 25.00 125.002 304.80 164.01 30.00

[0092] 2. The More Difficult Case

[0093] Three cells, each one moves a bit to the right. This sounds thesame as #1, but now the proximity of the new locations makes thesituation confusing: a A b B c C

[0094] This situation is almost the same as the simple example, butforms a major tracking problem. For example: A and C are closer to bthan to a or c. Using the simple proximity method, the following resultsare obtained:

[0095] a is lost, b moved to A or C, B is a “new cell” and c moved to A

[0096] This situation requires the more complex proximity matrix andcompetitive matching portion of the algorithm to come up with a bestglobal fit for all cells involved. The algorithm can track this, byusing the Distance Minimization function of the algorithm. (See below)

[0097] // Program output . . .

[0098] Test 4 . . . 3 cells move to the right, but are too close forsimple matching Previous Image: Label Xcm Ycm CellID 0 100 100 1 1 125100 2 2 105 105 3 Simple proximity method: New Image: Label Xcm YcmCellID Quality New? dX dY Distance 0 120 100 2 100% Old −5 0 5.00 1 145100 3  12% Old 40 −5 40.00 2 125 105 1 100% Old 2 5 25.00 Simpleproximity method failed Now look at the distance matrix more closely:Distance matrix: 0 1 2 0 20.00 5.00 15.81 1 45.00 20.00 40.31 2 25.505.00 20.00 matching permutation computed total distance weight 0 1 2 | || 0 1 2 20.00 + 20.00 + 20.00 = 60.0 0 2 1 20.00 + 5.00 + 40.31 = 65.311 0 2 45.00 + 5.00 + 20.00 = 75.0 1 2 0 45.00 + 5.00 + 15.81 = 65.81 2 10 25.50 + 20.00 + 15.81 = 61.31 2 0 1 25.50 + 5.00 + 40.31 = 75.81 Hence0 1 2 is the best matching sequence Total Distance Minimization method:New Image: Xcm Ycm CellID Quality New? dX dY Distance 0 120 100 0 100%Old 20 0 20.00 1 145 100 1  12% Old 20 0 20.00 2 125 105 2 100% Old 20 020.00

[0099] So How Do We Know We Could Be Dealing with an Example 2 Insteadof 1?

[0100] If there are lots of close contenders in the area, one couldsimply assume the simple proximity method will run in to itslimitations.

[0101] Secondly, Create and Destroy events suggest the existence of an“aliasing” effect. It may be difficult to distinguish Create and Arrivalevents if the sampling frequency is too low to make a proper judgment.At a low sampling frequency, a cell that suddenly appears near theboundary could have been a creation, or could simply have moved inquickly as an arrival. The same applies for Destroy and Departureevents. Note that an Arrival may also occur if a cell enters the FOV“from above”. This means a cell is floating higher than the depth offield and lands in the FOV. Most observed Create and Destroy events arecaused by artifacts, such as a focus or signal-to-noise problem. If theproblem is corrected in a subsequent time point, the same cell will showup as a Create event.

[0102] For example:

[0103] a Ab B

[0104] The simple conclusion would be:

[0105] a is destroyed, b moves to A, and B is new creation.

[0106] In addition to the Create and Destroy clues, one can use an AverLastMoveDist value to define a “sphere of influence”.

[0107] Last Distance Moved

[0108] To assess the amount of movement expected from an individualcell, the distance moved from the previous time point can be recorded.Although past performance is not a true indication of how much the cellmay move now, it is better than no indication at all.

[0109] In a preferred embodiment, at time point t, this is defined as:

LastMoveDist_(t)=SQRT((posx _((t−2)) −posx _((t−1)))²+(posy _((t−2))−posy _((t−1)))²)

[0110] This value needs to be set it with a value that reflectsexpectation at the beginning of the image acquisition set. For example,control experiments can be run, or a reasonable expectation for thatvalue can be provided.

[0111] Rolling Average of Last Distance Moved

[0112] Since the motion of a cell can seem erratic, it is preferred toaverage a few time points rather than using a single time point. Thus, afurther preferred embodiment comprises carrying forward a rollingaverage of distance moved to each new time point.

[0113] In a preferred embodiment, at time point t, this is defined as:

[0114] Average LastMoveDist_(t)=(1−k)·LastMoveDist_((t−1))+k·LastMoveDist_(t)

[0115] Where k is constant to define the weight factor of this geometricaverage. The choice of k depends on the sampling frequency relative tothe amount of change in the cells, and on the desired amount ofsmoothing of the feature over time. A value of k close to 1 will dolittle or no smoothing, while a value close to zero will do a lot ofsmoothing. The Average LastMoveDist value needs to be set with a valuethat reflects expectation at the beginning of the image acquisition set.For example, control experiments can be run, or a reasonable expectationfor that value can be provided.

[0116] In a preferred embodiment, identifying cells or groups of cellsthat require analysis by the confusion matrix comprises:

[0117] 1. Find the largest Average LastMoveDist of all cells in thisfield. This is a good indication of the motility of these cells. Thisnumber can be multiplied by a safety factor, for example by 1.3, toallow for e.g. 30% more motility than was previously seen. The only costof increasing this number is computation time.

[0118] 2. For each cell, compute a sphere of influence using thisinflated Average LastMoveDist number. The purpose is to generate a largeenough sphere, to assure not generating false Create and Destroy Events.However, the sphere is small enough (i.e.: preferably 10 cells or less)so that the confusion matrix of all cells inside the sphere of influencedoes not become so large that it becomes too computationally intensive.

[0119] 3. Merge spheres by propagation if they overlap. For example,when two spatially distinct clusters of cells share only one cell thatis close enough be part of either cluster, those clusters need to bemerged into one.

[0120] 4. The spheres result in groups of cells that “may have somethingto do with each other.” They are not really spheres anymore by thattime, just a list of cell ID's. Any time that there is more than onecontender in a sphere, it can be assumed that the simple proximitymethod is inadequate, and the more complex matching methods areutilized.

[0121] Confusion Matrix

[0122] If the previous step identifies the need for complex matching, aconfusion matrix can be computed. In one embodiment, the confusionmatrix is conducted on small groups of cells, preferably less thantwenty cells, and even more preferably fifteen cells or fewer.

[0123] For example, if there are three cells in the group, a vector suchas the one below is created: MM_(1,1) MM_(1,2) MM_(1,3) MM_(2,1)MM_(2,2) MM_(2,3) MM_(3,1) MM_(3,2) MM_(3,3)

[0124] where MM_(1,2) is the MisMatch of Cell 1 compared with Cell 2,etc. The computed distance between the cells can be added to the MMmatrix elements at this point and use it in the same computation asanother matching feature.

[0125] Real Arrival/Departure and Create/Destroy Events

[0126] Using the above matrix will always generate a match, even ifthere are Arrival/Departure and create /destroy events.

[0127] It is preferred that there be a limit at which a match isrejected, and at that point a create and/ or a destroy event is present.The average quality score can be used for this purpose. This figure canbe multiplied by an allowance factor to come up with a threshold value.The “allowance factor” is preferably arrive at by balancing thelikelihood of a Create/Destroy event with the performance of thetracking precision. The threshold can also be set externally, if enoughlearning data sets of specific cell types and assays have been generatedby which to establish an appropriate threshold.

[0128] Reduction of the Confusion Matrix

[0129] The confusion matrix can become very hard to solve if the numberof cells in a confusion cluster is larger than about 10-20 cells. Thenumber of permutations that need to be evaluated is proportional to thefactorial of the number of cells in the cluster. This can be avoided bysetting the maximum reasonable distance between cells low enough, andusing a sampling frequency that is appropriate, based on previous testdata and setting new standards for the preparation and assay parameters.Use of this “matrix reduction method” allows handling of largerconfusion matrices of, for example, 20-40 cells, at a fraction of thecomputational time.

[0130] Alternatively, the efficiency of solving the confusion matrix canbe increased by using the distance matrix to “pre-screen” the confusionmatrix elements. This method involves excluding any cell identificationmatches with a quality score at or above a user-defined threshold forquality scores (as determined by the distance matrix), from theconfusion matrix.

[0131] The cell tracking methods disclosed herein provide information onthe continuity of cell identification from time point to time point in akinetic cell screening assay. To integrate the information with a cellscreening assay(s), the results from the cell tracking methods arepreferably managed so that cell and well features, and kinetic outputfeatures, can be associated with the correct cells, Relating assayoutput features to cell identification requires additional datamanagement. Optimal computation of kinetic features (cell-based orwell-based) depends on a cell data management algorithm (FIG. 1) thatworks in conjunction with the cell tracking module. The data managementserves three important purposes: (1) to dynamically relate the list ofoutput features and cell ID's to each other; (2) to enable modificationof the assay output data by the results of cell tracking; (3) to enablethe correct sorting of data sets obtained from multiple images. Forexample, assay data may be eliminated for invalid cells. Cells may bemarked as invalid if for example they are present at some time pointsbut not other time points.

[0132] At each time point the cell data need to be rearranged inaccordance with current cell ID (kinetics cell ID) so that cell kineticdata can be computed. Then, the kinetic data need to be realigned withcell ID's again for well statistics to be computed. Statistics can bedone on all cells in a well or only on fully tracked cells, depending onthe needs of the user. The data management algorithm keeps track of allnewly identified cells (at any current time point), thus, allowing theuser to identify the time interval (starting time point and ending timepoint) during which each cell has been tracked. This, in its turn, makesit possible to flag cells that were fully tracked from the beginning ofthe experiment to the end of it. The ability to select cells that fitcertain cell ID criteria is valuable for producing optimal kinetic dataon the cell level. While population averaged data may be minimallyaffected by the loss or gain of a few cells, the cell level kinetic datacan be dramatically affected by mis-identification or by cells that arenot detectable throughout the entire experiment. In another aspect, thepresent invention comprises computer readable storage medium comprisinga program containing a set of instructions for causing a cell screeningsystem to execute procedures for tracking individual cells during akinetic cell screening assay, wherein the procedures comprise thevarious method steps of the invention. The computer readable mediumincludes but is not limited to magnetic disks, optical disks, organicmemory, and any other volatile (e.g., Random Access Memory (“RAM”)) ornon-volatile (e.g., Read-Only Memory (“ROM”)) mass storage systemreadable by the CPU. The computer readable medium includes cooperatingor interconnected computer readable medium, which exist exclusively onthe processing system or be distributed among multiple interconnectedprocessing systems that may be local or remote to the processing system.

[0133] In a preferred embodiment, the cell screening system comprises afluorescence optical system with a stage adapted for holding cells and ameans for moving the stage, a digital camera, a light source forreceiving and processing the digital data from the digital camera, and acomputer means for receiving and processing the digital data from thedigital camera. This aspect of the invention comprises programs thatinstruct the cell screening system to define the organization of thecellular component(s) of interest in individual cells, using the methodsdisclosed herein.

[0134] The methods of the invention can be used in conjunction with anycell-based screening assay, including multiparametric assays, that canbenefit from kinetic analysis. A series of biologically importantmetabolites, regulatory molecules, and organelles (such as those shownin Table I), can be labeled with fluorophores and activity orconcentrations determined by measuring intensity changes over time. Amajority of these indicators require intact, living cells whichinherently change over time. Therefore, single cell kinetic intensitymeasurements are required for high content information from theseindicators. Most of the small molecule indicators listed in Table I(including trademarked indicators) are available from Molecular Probes.TABLE 1 Intensity Based Indicators of Biomolecidar Activity TargetFluorescent Indicator Ca²⁺ Fluo4, FLIPR, Indo1, Fura-2 Mg²⁺ Mg-Fura-2Na⁺ SBFI K⁺ PBFI Cl⁻ SPQ Metal Ions: Calcein, Calcium Green-1, BTC-5N,Zn²⁺, Cu⁺, Cu²⁺, Cd²⁺.Hg²⁺, Ni⁺, FITC_Gly, —His, TCCP, TSPP, APTRA-BTCCo²⁺, Pb²⁺, Fe²⁺, Fe³⁺, Ba²⁺, As³⁺, Tb³⁺, La²⁺ pH BCECF, SNARF, SNAFL,NERF Gene Expression GFP-cDNA chimera with gene of choice Proliferationand DNA content Hoecsht, DAPI Viability Live/Dead dyes such as CMFDA orCalcein (live)/Propidium Iodide (dead) Membrane Potential DiBAC Cellularorgandies MITOTRACKER ™ , JC-1,, LYSOTRACKER ™ , Fluorescein-Dextran,Carbocyanin and ceramide dyes Nitric Oxide/Reactive OxygenChloro-Fluorescein Species Phosphoinositides Bodipy-Inositol Cyclic AMPPKA Chimeras and covalently labeled proteins Multi Drug Resistancetransporter Doxorubicin, Rhodamine −123 Protease activity Amino-coumarinsubstrate peptides Cell Surface and Intracellular Various FluorescentLigands Receptors

[0135] Ligand Binding

[0136] Ligands for cell surface receptors bind specific extracellularligands. Some native ligands induce molecular function while otherexogenous molecules such as drugs bind, partition in subcellularcompartments and modulate biomolecule function. Ligands that arefluorescently labeled can be monitored for binding to the cell.Fluorescent EGF binding to Epidermal Growth Factor Receptor occurswithin a few minutes, activating the receptor. After surface binding,the EGF- receptor complex internalizes into endosomal compartments,indicating down-regulation and termination of the signal. Binding andinternalization can be detected using the kinetic methods of theinvention.

[0137] Cell Viability

[0138] Intact plasma membranes can be detected by introducing indicatorsthat pass through intact cell membranes and are trapped intracellularlyby enzymatic removal of side groups needed for membrane permeability.Dyes remain trapped, labeling cells, unless the plasma membrane isruptured, releasing the internalized dyes. Acetoxymethyl esterderivatives of calcein work well as indicators of intact cell membranesand viable cells. Ongoing viability of the cells can be monitored inconjunction with the kinetic methods of the invention.

[0139] GFP Expression

[0140] The kinetic methods of the invention can be used to monitorexpression of proteins over time. Many proteins can be fluorescentlylabeled without perturbing function by making DNA constructs of theprotein of interest that contains additional code for a GreenFluorescent Protein (GFP). These bioreporters are expressed in cells toproduce functional protein that is fluorescently labeled. These probeswould be useful as i) a target validation tool with which the levels ofpotential therapeutic targets expressed in genetically engineered cellscould be monitored, or ii) a screening tool with which the effects ofcompounds on levels of GFP-[Promoter of Interest] fusion proteins couldbe monitored. The time of response is on the order of hours to days.

[0141] Nitric Oxide/Reactive Oxygen Species

[0142] Nitric Oxide is an important signaling molecules in neuron andendothelial cells and controls vascular tone, and cell communication.This application could be used as a screening tool, or as a cytotoxicitytool to monitor production of reactive oxygen species. These moleculesare important pharmacological targets for stroke, Alzheimer's disease,Parkinson's disease and congestive heart failure. The time of responseis on the order of 1-10 minutes, and thus could be developed using thekinetic methods of the invention.

[0143] Multiple Drug Resistance (MDR)

[0144] This application can be used to monitor the activity of the cellsurface transporter, P-glycoprotein. This is a molecular pump that isembedded in plasma membrane and pumps anticancer drugs out of cells,rendering the cells resistant to a wide variety of therapeutic agents.The time response for this assay is on the order of minutes, and thuscould be developed using the kinetic methods of the invention. Thisassay would be applicable to anticancer therapies.

[0145] Lysosome pH

[0146] Fluorescein labeled dextrans are taken up into the cell byendocytosis and end up in lysosomal compartments where the dextrans aredegraded. The intensity of fluorescein is pH dependent and so measuringintensity over time is sufficient to detect changes in lysosomalactivity induced by drugs such as the proton ionophore, monensin.

[0147] In a preferred embodiment of the use of the kinetic methods ofthe invention in conjunction with a cell screening assay, cells aresegmented by contacting the cells with a nuclear label and usinginformation from the nuclear channel. Images from the nuclear channelcan optionally be preprocessed (shade corrected and smoothed) and arethresholded (using an automatic thresholding procedure), producing anuclear mask. By drawing lines equidistant to nuclei edges (water shedapproach) the nuclear zones of influence (non touching cellular domainmasks) are identified and the mask of the domains is created. For eachnuclear mask, an extended nuclear mask is created (nucleus mask isdilated a number of times that is dependent upon the cell type andsize). The logical “AND” of the mask with corresponding cellular domainresults in a final mask that is then applied to the second channel tomeasure the fluorescence intensity of the relevant fluorescent markerunder the mask. Nuclei are masked and cells are segmented by definingdomains outside of each nuclei with a watershed routine. Kineticfeatures are then determined, based on the changes in intensity inindividual cells from one measurement to the next, as described above.

[0148] By determining the intensity of the fluorescence emitted by themarkers in individual cells at various time points, the method providescell-based, kinetic measurements of one or more of the following:

[0149] Dynamic changes in intensity over time

[0150] Heterogeneity of intensity among cells

[0151] Repetitive oscillations in intensity

[0152] Waves of intensity changes through connected cells

[0153] Subpopulations of responding cells

[0154] Sequential activation of signaling molecules

[0155] In a preferred embodiment, the method provides a quantal responseof cells (i.e.: percent of responding cells with an intensity above athreshold value), which increases the value of the present assay overthose assays that measure only the raw amplitude of response. Thethreshold to be used for a particular parameter can be determined foreach time point, and the value(s) of the thresholds can be set beforethe scan as an assay input parameter, or can be reset during dataanalysis.

[0156] In a further preferred embodiment of the invention, the kineticmeasurement is modified, sorted, and/or excluded depending on thequality score for the cell identification match for each cell. Sortingincludes pooling data for all cells of some group, such as fast cells,cells on the 5th image set, cells with red markers, and subpopulationsof large cells.

[0157] In the case of calcium assays, kinetic features that can bedetermined include, but are not limited to:

[0158] Cell-based Kinetic Features:

[0159] Intensity—Cell averaged fluorescent intensity averaged over time

[0160] Prestim Intensity—the baseline intensity value prior tostimulation by agonist (averaged value over all prestim points)

[0161] Peak Intensity—Peak intensity value (Highest point or curve fitto find inflection point)

[0162] Relative Peak Intensity Value—Peak Intensity/Prestim Intensity.

[0163] Time to Peak Intensity

[0164] Plateau Intensity

[0165] Relative Plateau Intensity

[0166] Integrated Intensity of Ca2+Signaling

[0167] Oscillation frequency

[0168] Oscillation persistence

[0169] Oscillation amplitude

[0170] Well-based Kinetic Features:

[0171] Avg Fluorescent Intensity

[0172] Avg Baseline Intensity

[0173] Avg Peak Intensity

[0174] Avg Relative Peak Intensity

[0175] Avg Time to Peak Intensity

[0176] Avg Plateau Intensity to plateau and asymptote

[0177] Avg Relative Plateau Intensity

[0178] Avg Integrated Intensity of Ca2+Signaling

[0179] Avg Oscillation Frequency

[0180] Avg Oscillation Persistence

[0181] Avg Oscillation Amplitude

We claim:
 1. The present invention provides methods and software fortracking individual cells during a kinetic cell screening assay,comprising: a) providing cells that possess at least a firstluminescently labeled reporter molecule that reports on a cellstructure; b) obtaining a structure image from luminescent signals fromthe at least first luminescently labeled reporter molecule in the cellsin a field of view; c) creating a structure mask for individual cells inthe field of view; d) defining a reference point of each structure mask;e) assigning an cell identification to each reference point in the fieldof view; f) repeating steps (b) through (e) at a second time point; g)correlating cell identification between the first time point and thesecond time point by calculating a distance between reference points inthe field of view at the first time point and reference points in thefield of view at the second time point; and h) defining a cellidentification match by identifying reference points in the field ofview at the first time point and reference points in the field of viewat the second time point that are closest together.
 2. The method ofclaim 1, further comprising repeating steps (f)-(h) a desired number oftimes, wherein determining the distance between reference points is doneby determining a distance between reference points in successive timepoints, and wherein defining the closest cell identification match isdone by defining the closest cell identification match in successivetime points.
 3. The method of claim 1 wherein a cell identificationmatch is rejected if the cells identified as a cell identification matchare farther apart than a user-defined limit.
 4. The method of claim 3further comprising assigning a quality score to the cell identificationmatch based on a distance determined for a second closest cellidentification match, and wherein a cell identification match isrejected if the quality score is below a user-defined threshold for aquality score.
 5. The method of claim 4 wherein the quality score iscalculated by a method comprising dividing the difference between thedistance between reference points in the closest cell identificationmatch from the distance between reference points in the second closestcell identification match by the distance between reference points inthe second closest cell identification match.
 6. The method of claim 1further comprising determining a total sum of all distances or distancessquared for all possible cell identification matches in successive timepoints, wherein a smallest total sum of all distances or distancessquared is defined as a closest set of cell identification matches. 7.The method of claim 6 further comprising assigning a quality score tothe cell identification match based on a sum of distances or distancessquared determined for a second closest cell identification match, andwherein a cell identification match is rejected if the quality score isbelow a user-defined threshold for a quality score.
 8. The method ofclaim 7 further comprising excluding from the determining a total sum ofall distances or distances squared for all possible cell identificationmatches in successive time points any cell identification matches with aquality score at or above a user-defined threshold for quality scores.9. The method of claim 8 wherein defining the cell identification matchfurther comprises comparing other features of the individual cellsbetween successive time points.
 10. The method of claim 9 wherein thefeatures comprise one or more features selected from the groupconsisting of area, shape, size, and luminescent intensity of cell orsubcellular structures, and exogenous tags associated with cell orsubcellular structures.
 11. The method of claim 10 wherein the featurecomprises an exogenous tag, and wherein the exogenous tag comprises abar coding tag.
 12. The method of claim 10 further comprising excludingany cell identification matches with a quality score at or above auser-defined threshold for quality scores from the comparing othercharacteristic features of the individual cells between successive timepoints. 13 The method of claim 10 further comprising excluding any cellidentification matches with a quality score below a user-definedthreshold for quality scores from the comparing other characteristicfeatures of the individual cells between successive time points.
 14. Themethod of claim 4 wherein the quality score is averaged over multipletime points and applied to a later time point as an average qualityscore.
 15. The method of claim 7 further comprising determining adistance moved by the individual cell in successive time points. 16 Themethod of claim 15 further comprising determining an average distancemoved by an individual cell over multiple time points.
 17. The method ofclaim 1 wherein the cell structure is a nucleus, wherein the structureimage is a nuclear image, and wherein the structure mask is a nuclearmask.
 18. The method of claim 1, wherein the cell screening assaycomprises one or more assays for the kinetic analysis of a cellparameter selected from the group consisting of ionic concentration, pH,gene expression, DNA proliferation, DNA content, cell viability,membrane potential, production of reactive oxygen species, enzymeactivity, receptor activation, ligand binding, and transporter activity.19. The method of claim 18, wherein the cells further possess at least asecond luminescently labeled reporter molecule that reports on the cellparameter, and wherein the method further comprises obtainingluminescent signals from the second luminescently labeled reportermolecule and calculating a kinetic measure of the luminescent signalsfrom the second luminescently labeled reporter molecule in individualcells, wherein the kinetic measure is selected from the group consistingof dynamic changes in intensity over time, heterogeneity of intensityamong cells, oscillations in intensity, waves of intensity changesthrough connected cells, subpopulations of responding cells, andsequential activation of signaling molecules.
 20. The method of claim19, wherein the kinetic measure is modified, sorted, and/or excludeddepending on a quality score for the cell identification match for eachcell.
 21. A computer readable storage medium comprising a programcontaining a set of instructions for causing a cell screening system toexecute procedures for tracking individual cells during a kinetic cellscreening assay, wherein the procedures comprise a) providing cells thatpossess at least a first luminescently labeled reporter molecule thatreports on a cell structure; b) obtaining a structure image fromluminescent signals from the at least first luminescently labeledreporter molecule in the cells in a field of view; c) creating astructure mask for individual cells in the field of view; d) defining areference point of each structure mask; e) assigning an cellidentification to each reference point in the field of view; f)repeating steps (b) through (e) at a second time point; g) correlatingcell identification between the first time point and the second timepoint by calculating a distance between reference points in the field ofview at the first time point and reference points in the field of viewat the second time point; and h) defining a cell identification match byidentifying reference points in the field of view at the first timepoint and reference points in the field of view at the second time pointthat are closest together.